Vol 2, No 1







Published

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Table of Contents

Articles

by Yisheng Wang, Yuzhan Lu
254 Views, 113 PDF Downloads

The purpose of this study is to explore in depth the special context and unique experience of the live video streaming and to provide insights regarding an interpretation of the contextualization experiences model. This study uses grounded theory, depth interviews, and the physical travel of researchers to the field for participation and observations. Finally, the insight of the live broadcast platform contextualization was developed. The theoretical contribution of this study is to establish the words of mouth relationship of the live broadcast platform and ten related propositions. The study revealed the mystery of live video streaming.


Articles

by Lixiu Hao, Weiwei Yu
114 Views, 33 PDF Downloads

Objective Face recognition can be affected by unfavorable factors such as illumination, posture and expression, but the face image set is a collection of people’s various angles, different illuminations and even different expressions, which can effectively reduce these adverse effects and get higher face recognition rate. In order to make the face image set have higher recognition rate, a new method of combining face image set recognition is proposed, which combines an improved Histogram of Oriented Gradient (HOG) feature and Convolutional Neural Network (CNN). Method The method firstly segments the face images to be identified and performs HOG to extract features of the segmented images. Secondly, calculate the information entropy contained in each block as a weight coefficient of each block to form a new HOG features, and non-negative matrix factorization (NMF) is applied to reduce HOG features. Then the reduced-dimensional HOG features are modeled as image sets which keep your face details as much as possible. Finally, the modeled image sets are classified by using a convolutional neural network. Result The experimental results show that compared with the simple CNN method and the HOG-CNN method, the recognition rate of the method on the CMU PIE face set is increased by about 4%~10%. Conclusion The method proposed in this paper has more details of the face, overcomes the adverse effects, and improves the accuracy.


Articles

by Xinzhi Yang
83 Views, 56 PDF Downloads

This paper, by means of literature and element analysis, discusses relevant literature in different fields such as cognitive neuroscience, human-computer interaction and brain-computer interface. Firstly, the development process of cognitive neuroscience is briefly introduced, and the evolution process of brain functional imaging technology is described. Secondly, the paper discusses the supporting effect of research results in cognitive neuroscience on human-computer interaction research. Then, the development of cognitive models for human-computer interaction research is described based on cognitive neuroscience and brain imaging technology, and some human-computer interaction researches from the perspective of cognitive neuroscience and brain imaging are briefly introduced.


Articles

by Wencheng Dong
45 Views, 29 PDF Downloads

Human-computer interaction system is the medium for communicating and transmitting information between people and computers. With the rapid development of computer technology, traditional human computer interaction technologies such as mouse and keyboard, have not met the needs of the development of the times. Instead, people need another human-computer interaction technology which is faster, more natural and comfortable. Gesture-based human-computer interaction is one of the most important technologies in human-computer interaction system. There are problems remained in traditional gesture recognition methods, such as low recognition accuracy and complicated recognition process. In view of the defects above, this paper proposes a gesture recognition algorithm based on deep learning. The algorithm detects joint features of the gesture quickly through gesture estimation and classifies joint feature maps by using convolution neural network, which overcomes the difficulties of segmenting gesture images in complex background and improves the accuracy of recognition results. The experimental results indicate that the method has high recognition accuracy for various gestures at different scales, which reaches 98%. Finally, a human-computer interaction system is designed based on the algorithm, and the application of gesture recognition in the human-computer interaction system is demonstrated.


Articles

by Wenbin Du
38 Views, 33 PDF Downloads

A design method based on information fusion is proposed regarding the problem of poor integration and intelligence of human-computer interface for current laser cutting machine. The designed human-computer interaction information system of laser cutting machine is divided into laser cutting parameter calculation module, MVB integrated control module, bus control module and information interface interaction module. Embedded ARM and extended bus technology are adopted to carry out human-computer interaction information transmission and resource scheduling of laser cutting machine under the Internet of Things environment, and a human-computer interaction information database is constructed. GUI rendering technology is adopted to design the human-computer interaction information interface. RTCP control structure word of service quality monitoring is combined to carry out information fusion processing of the human-computer interaction information system of laser cutting machine, and centralized control method is adopted to design the human-computer interaction interface of the machine. Simulation results show that the visual effect of using this method to design the human-computer interaction information interface of laser cutting machine is better, and the precision and human-computer interaction of laser cutting are improved.